Forward Energy Transactions for Machine Fleet Resource Forecasting
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Solution Overview
Problem
There is a need for systems and methods that improve the efficiency, speed, and reliability of machines involved in market activities, particularly in distributed ledger transactions, energy, compute, and resource allocation, while addressing the challenges of energy consumption and volatility in renewable resources and market uncertainties.
Innovation Solution
A transaction-enabling system that includes a resource requirement circuit to aggregate resource needs, a forward market circuit for energy transactions, and a machine resource acquisition circuit, utilizing machine learning and AI to adaptively improve resource utilization and cost efficiency through predictive market forecasting and data interpretation from various external sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machines perform high-performance computing operations for market activities and distributed ledger transactions, then processing speed and transaction capability are improved, but energy consumption increases
Solution Approach 1:
The system performs preliminary actions by forecasting energy prices and predicting market conditions before executing transactions. Energy-intensive computing operations are scheduled and executed at optimal times based on predicted energy costs, reducing overall energy consumption while maintaining productivity
Solution Approach 2:
The system dynamically adjusts computing operations based on real-time energy price signals and market conditions. Computing workloads are shifted flexibly across different time periods and locations, optimizing the balance between transaction execution speed and energy consumption
2Productivity
If the system aggregates resource requirements for machine fleets and executes transactions on forward markets, then resource allocation efficiency is improved, but system complexity increases
Solution Approach 1:
The system employs universal smart contracts that can handle multiple types of resource transactions (energy, compute, storage) through standardized protocols. This multi-functional approach simplifies the overall system architecture by using common frameworks for diverse resource management tasks
Solution Approach 2:
Smart contracts serve as intermediaries between resource requirements and forward market transactions. These automated contracts manage the complexity of resource aggregation, pricing, and execution, reducing the burden on the overall system while improving allocation efficiency
3Adaptability or versatility
If machine learning and AI are used to predict market prices and optimize resource utilization, then cost efficiency is improved, but computational overhead increases
Solution Approach 1:
The system applies machine learning and AI selectively to the most critical prediction tasks rather than comprehensively to all operations. This partial application of advanced algorithms optimizes cost efficiency for key decisions while limiting computational overhead to manageable levels
Solution Approach 2:
Complex computational optimization tasks are replaced with automated smart contract execution based on pre-trained machine learning models. The heavy lifting of pattern recognition and prediction is done offline during model training, while runtime operations use simpler inference processes
Data Source
AI summary
Systems and methods for machine forward energy and energy storage transactions are disclosed. An example transaction-enabling system may include a resource requirement circuit to aggregate a resource requirement for a fleet of machines to perform a task, wherein the resource requirement comprises an energy storage capacity requirement, a forward resource market circuit to access a forward market for energy, and a machine resource acquisition circuit to execute a transaction on the forward market for energy in response to the aggregated resource requirement.